Development of Clinical Pharmacy Key Performance Indicators for Hospital Pharmacists Using a Modified Delphi Approach
Bibliographic record
Abstract
BACKGROUND: Key performance indicators (KPIs) are quantifiable measures of quality. There are no published, systematically derived clinical pharmacy KPIs (cpKPIs). OBJECTIVE: A group of hospital pharmacists aimed to develop national cpKPIs to advance clinical pharmacy practice and improve patient care. METHODS: A cpKPI working group established a cpKPI definition, 8 evidence-derived cpKPI critical activity areas, 26 candidate cpKPIs, and 11 cpKPI ideal attributes in addition to 1 overall consensus criterion. Twenty-six clinical pharmacists and hospital pharmacy leaders participated in an internet-based 3-round modified Delphi survey. Panelists rated 26 candidate cpKPIs using 11 cpKPI ideal attributes and 1 overall consensus criterion on a 9-point Likert scale. A meeting was facilitated between rounds 2 and 3 to debate the merits and wording of candidate cpKPIs. Consensus was reached if 75% or more of panelists assigned a score of 7 to 9 on the consensus criterion during the third Delphi round. RESULTS: All panelists completed the 3 Delphi rounds, and 25/26 (96%) attended the meeting. Eight candidate cpKPIs met the consensus definition: (1) performing admission medication reconciliation (including best-possible medication history), (2) participating in interprofessional patient care rounds, (3) completing pharmaceutical care plans, (4) resolving drug therapy problems, (5) providing in-person disease and medication education to patients, (6) providing discharge patient medication education, (7) performing discharge medication reconciliation, and (8) providing bundled, proactive direct patient care activities. CONCLUSIONS: A Delphi panel of hospital pharmacists was successful in determining 8 consensus cpKPIs. Measurement and assessment of these cpKPIs will serve to advance clinical pharmacy practice and improve patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.141 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".